Skin lesion segmentation using high-resolution convolutional neural network
Skin lesion segmentation using high-resolution convolutional neural network
复制标题
使用高分辨率卷积神经网络进行皮肤病变分割
DOI：
10.1016/j.cmpb.2019.105241
复制
发表时间：
2020-04-01
影响因子：
6.1
通讯作者：
Wang, Yukun
中科院分区：
文献类型：
--
作者：
Xie, Fengying;Yang, Jiawen;Wang, Yukun
背景与目的：皮肤病损分割是皮肤镜图像计算机辅助诊断中一个重要而又具有挑战性的课题。许多基于卷积神经网络的分割方法通常无法提取准确的病变边界，因为随着在整个网络层中处理特征图，特征图的空间大小会减小。我们提出了皮肤镜图像的皮肤病变分割的卷积神经网络的基础上的注意力机制，它可以保持边缘details.Methods：我们设计了一个高分辨率的功能块，包含三个分支，即，主要的，空间的注意力，和通道明智的注意力分支。主分支将高分辨率特征地图作为输入来提取边界周围的空间细节。另外两个注意力分支增强了主分支中关于空间和通道维度的区分特征。通过融合分支输出，可以提取具有详细空间信息的鲁棒特征，并且可以获得准确的皮肤病变边界。结果：在2016年和2017年国际生物医学成像研讨会的数据集和PH 2数据集上的实验检索到的Jaccard指数分别为0.783、0.858和0.857。因此，我们的方法可以准确地提取皮肤病变的边界，是强大的头发纤维和文物的图像。总体而言，我们的方法优于两个典型的分割网络（FCN-8和U-Net）和其他国家的最先进的皮肤病变segmentation methods.Conclusions：所提出的网络赋予高分辨率的特征块保留空间细节在特征提取过程中，其注意力机制提高了代表性的功能，同时抑制噪声。因此，所提出的方法提供了高性能的皮肤病变分割。(C)2019由Elsevier B. V.出版
Background and Objective: Skin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation methods based on convolutional neural networks often fail to extract accurate lesion boundaries because the spatial size of feature maps decreases as the maps are processed throughout the network layers. We propose skin lesion segmentation in dermoscopy images based on a convolutional neural network with an attention mechanism, which can preserve edge details.Methods: We devised a high-resolution feature block containing three branches, namely, main, spatial attention, and channel-wise attention branches. The main branch takes high-resolution feature maps as input to extract spatial details around boundaries. The other two attention branches boost the discriminative features in the main branch regarding the spatial and channel-wise dimensions. By fusing the branch outputs, robust features with detailed spatial information can be extracted, and accurate skin lesion boundaries can be obtained.Results: Experiments on datasets from the International Symposium on Biomedical Imaging in 2016 and 2017 and the PH2 dataset retrieved Jaccard indices of 0.783, 0.858, and 0.857, respectively, for the proposed method. Hence, our method can accurately extract skin lesion boundaries and is robust to hair fibers and artifacts in the images. Overall, our method outperforms two typical segmentation networks (FCN-8 s and U-Net) and other state-of-the-art skin lesion segmentation methods.Conclusions: The proposed network endowed with high-resolution feature blocks preserves spatial details during feature extraction, and its attention mechanism enhances representative features while suppressing noise. Hence, the proposed approach provides high-performance skin lesion segmentation. (C) 2019 Published by Elsevier B.V.